Popular GitHub Repositories for Oxford

Oxford Deep NLP 2017 course

在 oxford hand 数据集上对 YOLOv3 做模型剪枝（network slimming）

Oxford Deep NLP 2017 course - Practical 1: word2vec

A Light CNN based Method for Hand Detection and Orientation Estimation

Oxford Deep NLP 2017 course - Practical 2: Text Classification

Hands video tracker using the Tensorflow Object Detection API and Faster RCNN model. The data used is the Hand Dataset from University of Oxford.

This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).

Sample app that predicts the flower type on an image, using Core ML and the Oxford 102 category flower dataset.

This Repository contains TensorFlow implementation of different Image Segmentation Architecture on different types of datasets.

Image segmentation on the oxford-iiit pet dataset using CNN's, GNN's and U-Nets

Oxford MSc thesis. PriorVAE with graph convolutional networks for learning locally-aware spatial prior distributions

A web app that segments the image of a pet animal into three regions - foreground (pet), background and boundary. It uses a U-Net model trained on Oxford-IIIT Pet Dataset and is deployed using Gradio.

Image Segmentation using Oxford Pets Dataset with the goal to improve animal tranquilizer aiming system.

A simple website that uses the Project Oxford Emotion API to find out who the happiest person in a group photo is

An iOS app written in Swift 4 that uses CoreML and the Oxford 102 category flower dataset to identify a flower taken with the camera and display a blurb from wikipedia about it.

Flower Power is an iOS 11 / Swift 4 based image recognition app that uses the Oxford 102 Category dataset as a pre-trained model for Core ML to classify what type of flower is in your photo.

I built a Python application that trained an image classifier on an Oxford flower dataset to recognize different species of flowers, and then predicted new flower images using the trained model. This project is a starting point in the world of deep learning and neural networks, implemented here using Keras, TensorFlow and transfer learning techniques.

针对非常小的数据集，本文对比研究了迁移学习与构建自定义卷积神经网络 (CNN) 架构的优劣。